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--- |
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license: apache-2.0 |
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language: |
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- am |
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- ti |
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- ha |
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- aa |
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base_model: |
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- Hailay/EXLMR |
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- FacebookAI/xlm-roberta-base |
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pipeline_tag: text-classification |
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--- |
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--- |
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## 1. Model Description |
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**Hailay/FT_EXLMR** is a fine-tuned version of the **EXLMR** model, designed specifically for sentiment analysis and text classification tasks in low-resource African languages such as Tigrinya, Amharic, and Oromo. This model leverages the architecture of EXLMR but has been further fine-tuned to improve its performance on multilingual tasks, especially for languages not widely represented in existing NLP models. |
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The model was trained using the AfriSent-Semeval-2023 dataset, a benchmark dataset for African languages, which is publicly available on GitHub:[AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023) |
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## 2.Intended Use |
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This model is ideal for: |
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Researchers and developers who are working on multilingual sentiment analysis in African languages. |
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Applications that require text classification in low-resource languages. |
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It is designed specifically for tasks such as: |
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Sentiment analysis |
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Text classification |
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**Note:** Without further fine-tuning, the model is unsuitable for tasks like machine translation or named entity recognition. |
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## 3.Training Data |
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The **Hailay/FT_EXLMR** model was trained using the dataset from the |
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**SemEval 2023 Shared Task 12: Sentiment Analysis in African Languages (AfriSenti-SemEval)**. |
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This dataset comprises sentiment-labeled text from 14 African languages: |
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1. Algerian Arabic (arq) - Algeria |
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2. Amharic (ama) - Ethiopia |
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3. Hausa (hau) - Nigeria |
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4. Igbo (ibo) - Nigeria |
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5. Kinyarwanda (kin) - Rwanda |
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6. Moroccan Arabic/Darija (ary) - Morocco |
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7. Mozambique Portuguese (pt-MZ) - Mozambique |
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8. Nigerian Pidgin (pcm) - Nigeria |
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9. Oromo (orm) - Ethiopia |
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10. Swahili (swa) - Kenya/Tanzania |
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11. Tigrinya (tir) - Ethiopia |
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12. Twi (twi) - Ghana |
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13. Xithonga (tso) - Mozambique |
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14. Yoruba (yor) - Nigeria |
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The dataset covers diverse data for training multilingual models like **Hailay/FT_EXLMR** |
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We access the dataset from [AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023). |
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The **Hailay/FT_EXLMR** model was trained using the following configuration: |
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Epochs: 3 |
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Learning Rate: 1e-5 |
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Optimizer: AdamW |
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Batch Size: 16 |
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## 4. Evaluation |
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The model was evaluated using accuracy and loss as the primary metrics. The results are as follows: |
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Accuracy: Achieved strong performance on Tigrinya, Amharic, Afar, and Oromo text classification and sentiment analysis tasks. |
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Loss: Loss values showed steady convergence during the 3 epochs of training, reflecting a well-calibrated model. |
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The evaluation was carried out on the test set provided in the [AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023) dataset. |
